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The MUSE Hubble Ultra Deep Field Survey: I. Survey description, data reduction and source detection, , , , , , , , , and 17 other author(s). (2017)cite arxiv:1710.03002Comment: 21 pages.Sparsity-based spatial-spectral restoration of muse astrophysical hyperspectral data cubes., , , and . WHISPERS, page 1-4. IEEE, (2011)Subspace learning in minimax detection., , and . ICASSP, page 3062-3066. IEEE, (2014)Restoration of Astrophysical Spectra With Sparsity Constraints: Models and Algorithms., , and . IEEE J. Sel. Top. Signal Process., 5 (5): 1002-1013 (2011)GLUP: Yet another algorithm for blind unmixing of hyperspectral data., , , and . WHISPERS, page 1-4. IEEE, (2014)A non-asymptotic standardization of binomial counts in Higher Criticism., and . ISIT, page 561-565. IEEE, (2014)Using hydrodynamical simulations of stellar atmospheres for periodogram standardization: Application to exoplanet detection., , and . ICASSP, page 4428-4432. IEEE, (2016)Detection Tests Using Sparse Models, With Application to Hyperspectral Data., , and . IEEE Trans. Signal Process., 61 (6): 1481-1494 (2013)Dimension Reduction for Hypothesis Testing in Worst-Case Scenarios., , and . IEEE Trans. Signal Process., 62 (22): 5973-5986 (2014)Detection of astrophysical sources in hyperspectral data. Applications to the MUSE instrument., , and . ICIP, page 6031-6035. IEEE, (2014)